



'''simple docstring'''






import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def  lowerCamelCase__			(		_A					,     _A					,     _A					,     _A					,     _A							):
  # Load configuration defined in the metadata file
  with open(_A							) as metadata_file:
    a		:   Union[str, Any]	  =	json.load(_A							)
  a		:   Tuple	  =	LukeConfig(use_entity_aware_attention=_A					,     **metadata['model_config']							)

  # Load in the weights from the checkpoint_path
  a		:   List[Any]	  =	torch.load(_A					,     map_location='cpu'							)

  # Load the entity vocab file
  a		:   Dict	  =	load_entity_vocab(_A							)

  a		:   Tuple	  =	RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']							)

  # Add special tokens to the token vocabulary for downstream tasks
  a		:   Optional[Any]	  =	AddedToken('<ent>'					,     lstrip=_A					,     rstrip=_A							)
  a		:   Dict	  =	AddedToken('<ent2>'					,     lstrip=_A					,     rstrip=_A							)
  tokenizer.add_special_tokens({'additional_special_tokens': [entity_token_a, entity_token_a]}							)
  config.vocab_size += 2

  print(f"""Saving tokenizer to {pytorch_dump_folder_path}"""							)
  tokenizer.save_pretrained(_A							)
  with open(os.path.join(_A					,     LukeTokenizer.vocab_files_names['entity_vocab_file']							)					,     'w'							) as f:
    json.dump(_A					,     _A							)

  a		:   Any	  =	LukeTokenizer.from_pretrained(_A							)

  # Initialize the embeddings of the special tokens
  a		:   Union[str, Any]	  =	state_dict['embeddings.word_embeddings.weight']
  a		:   int	  =	word_emb[tokenizer.convert_tokens_to_ids(['@']							)[0]].unsqueeze(0							)
  a		:   Dict	  =	word_emb[tokenizer.convert_tokens_to_ids(['#']							)[0]].unsqueeze(0							)
  a		:   Any	  =	torch.cat([word_emb, ent_emb, enta_emb]							)

  # Initialize the query layers of the entity-aware self-attention mechanism
  for layer_index in range(config.num_hidden_layers							):
    for matrix_name in ["query.weight", "query.bias"]:
      a		:   List[Any]	  =	f"""encoder.layer.{layer_index}.attention.self."""
      a		:   Tuple	  =	state_dict[prefix + matrix_name]
      a		:   str	  =	state_dict[prefix + matrix_name]
      a		:   Any	  =	state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
  a		:   Union[str, Any]	  =	state_dict['entity_embeddings.entity_embeddings.weight']
  a		:   List[Any]	  =	entity_emb[entity_vocab['[MASK]']]

  a		:   int	  =	LukeModel(config=_A							).eval()

  a  ,      a		:   int	  =	model.load_state_dict(_A					,     strict=_A							)
  if not (len(_A							) == 1 and missing_keys[0] == "embeddings.position_ids"):
    raise ValueError(f"""Missing keys {", ".join(_A							)}. Expected only missing embeddings.position_ids"""							)
  if not (all(key.startswith('entity_predictions'							) or key.startswith('lm_head'							) for key in unexpected_keys							)):
    raise ValueError(
        'Unexpected keys'
        f""" {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"							) or key.startswith("lm_head"							))]							)}"""							)

  # Check outputs
  a		:   Dict	  =	LukeTokenizer.from_pretrained(_A					,     task='entity_classification'							)

  a		:   List[str]	  =	(
      'Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'
      ' new world number one avoid a humiliating second- round exit at Wimbledon .'
  )
  a		:   Tuple	  =	(39, 42)
  a		:   Any	  =	tokenizer(_A					,     entity_spans=[span]					,     add_prefix_space=_A					,     return_tensors='pt'							)

  a		:   Any	  =	model(**_A							)

  # Verify word hidden states
  if model_size == "large":
    a		:   Union[str, Any]	  =	torch.Size((1, 42, 1024)							)
    a		:   int	  =	torch.tensor(
        [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]							)
  else:  # base
    a		:   Dict	  =	torch.Size((1, 42, 768)							)
    a		:   Optional[int]	  =	torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]							)

  if not (outputs.last_hidden_state.shape == expected_shape):
    raise ValueError(
        f"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"""							)
  if not torch.allclose(outputs.last_hidden_state[0, :3, :3]					,     _A					,     atol=1E-4							):
    raise ValueError

  # Verify entity hidden states
  if model_size == "large":
    a		:   int	  =	torch.Size((1, 1, 1024)							)
    a		:   Any	  =	torch.tensor([[0.0466, -0.0106, -0.0179]]							)
  else:  # base
    a		:   int	  =	torch.Size((1, 1, 768)							)
    a		:   Dict	  =	torch.tensor([[0.1457, 0.1044, 0.0174]]							)

  if not (outputs.entity_last_hidden_state.shape != expected_shape):
    raise ValueError(
        f"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
        f""" {expected_shape}"""							)
  if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]					,     _A					,     atol=1E-4							):
    raise ValueError

  # Finally, save our PyTorch model and tokenizer
  print('Saving PyTorch model to {}'.format(_A							)							)
  model.save_pretrained(_A							)
def  lowerCamelCase__			(		_A							):
  a		:   Tuple	  =	{}
  with open(_A					,     'r'					,     encoding='utf-8'							) as f:
    for index, line in enumerate(_A							):
      a  ,      a		:   Tuple	  =	line.rstrip().split('\t'							)
      a		:   Union[str, Any]	  =	index

  return entity_vocab


if __name__ == "__main__":
     lowerCAmelCase:   Dict					=		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
     parser.add_argument(
         '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
     )
     parser.add_argument(
         '--entity_vocab_path',
         default=None,
         type=str,
         help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
     )
     parser.add_argument(
         '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
     )
     lowerCAmelCase:   List[str]					=		parser.parse_args()
     convert_luke_checkpoint(
         args.checkpoint_path,
         args.metadata_path,
         args.entity_vocab_path,
         args.pytorch_dump_folder_path,
         args.model_size,
     )




'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
     from PIL import Image

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """Salesforce/blip-image-captioning-base"""
    lowercase__					=       (
        """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
        """image to caption, and returns a text that contains the description in English."""
    )
    lowercase__					=       """image_captioner"""
    lowercase__					=       AutoModelForVisionaSeq

    lowercase__					=       ["""image"""]
    lowercase__					=       ["""text"""]







    def __init__( self					:    Tuple		,     *__snake_case					:    int		,     **__snake_case					:    List[Any]      ):
      requires_backends(self		,     ['vision']      )
      super().__init__(*__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    Optional[int]		,     __snake_case					:    "Image"      ):
      return self.pre_processor(images=__snake_case		,     return_tensors='pt'      )







    def      lowercase_			( self					:    Optional[Any]		,     __snake_case					:    str      ):
      return self.model.generate(**__snake_case      )







    def      lowercase_			( self					:    Optional[Any]		,     __snake_case					:    Tuple      ):
      return self.pre_processor.batch_decode(__snake_case		,     skip_special_tokens=__snake_case      )[0].strip()